System and method for predicting the presence of an entity at certain locations

Abstract

A system for monitoring vehicle traffic may include a camera positioned to capture images within a license plate detection zone, wherein the images may represent license plates of vehicles. The system may include an electronic device identification sensor that detects and stores electronic device identifiers of electronic devices located within an electronic device detection zone, and a computing system that detects, using the images, a license plate ID of a vehicle, compares the license plate ID of the vehicle to a database of trusted vehicle license plate IDs, identifies the vehicle as a suspicious vehicle, the identification based at least in part on the comparison of the license plate ID of the vehicle to the database of trusted vehicle license plate IDs, and correlates the license plate ID of the vehicle with at least one of the plurality of stored electronic device identifiers.

What is claimed is: 
     
         1 . A method, comprising:
 receiving location data for a first entity, wherein the location data includes a plurality of location-time pairs;   identifying, using a range of time values based on a particular time, a subset of the plurality of location-time pairs;   determining, using the subset of the plurality of location-time pairs, respective probabilities that the first entity will be located in the respective locations specified in the subset of the plurality of location-time pairs; and   using the respective probabilities, predicting a location for the first entity at the particular time.   
     
     
         2 . The method of  claim 1 , wherein determining the respective probabilities includes:
 determining a number of times a given location occurs within the subset of the plurality of location-time pairs; and   generating, by dividing the number of times the given location occurs within the subset of the plurality of location-time pairs by a number of location-time pairs included in the subset, a particular probability of the respective probabilities.   
     
     
         3 . The method of  claim 1 , wherein predicting the location for the first entity includes determining a largest probability in the respective probabilities. 
     
     
         4 . The method of  claim 1 , further comprising, in response to determining a current location of the first entity is available:
 determining a second subset of the plurality of location-time pairs, wherein the plurality of location-time pairs correspond, at a current time, to a plurality of possible destinations from the current location, wherein the second subset of the plurality of location-time pairs includes a particular location-time pair corresponding to the current location and the current time; and   generating a plurality of probabilities that the first entity will be at corresponding destinations of the plurality of possible destinations at a next time subsequent to the current time.   
     
     
         5 . The method of  claim 1 , further comprising determining a plan to avoid a second entity based on a predicted location of the second entity and a predicted location of the first entity. 
     
     
         6 . The method of  claim 1 , further comprising adding, using data received from a mobile communication device associated with the first entity, a new location-time pair. 
     
     
         7 . A system, comprising:
 one or more memory circuits configured to store instructions; and   one or more processors configured to receive instructions from the one or more memory circuits and execute the instructions to cause the system to perform operations including:
 receiving location data for a first entity, wherein the location data includes a plurality of location-time pairs; 
 identifying, using a range of time values based on a particular time, a subset of the plurality of location-time pairs; 
 determining, using the subset of the plurality of location-time pairs, respective probabilities that the first entity will be located in the respective locations specified in the subset of the plurality of location-time pairs; and 
 predicting, using the respective probabilities, a first location for the first entity at the particular time. 
   
     
     
         8 . The system of  claim 7 , wherein determining the respective probabilities includes:
 determining a number of times a given location occurs within the subset of the plurality of location-time pairs; and   generating, by dividing the number of times the given location occurs within the subset of the plurality of location-time pairs by a number of location-time pairs included in the subset, a particular probability of the respective probabilities.   
     
     
         9 . The system of  claim 7 , wherein predicting the first location for the first entity includes determining a largest probability in the respective probabilities. 
     
     
         10 . The system of  claim 7 , wherein the operations further include, in response to determining a current location of the first entity is available:
 determining a second subset of the plurality of location-time pairs, wherein the plurality of location-time pairs correspond, at a current time, to a plurality of possible destinations from the current location, wherein the second subset of the plurality of location-time pairs includes a particular location-time pair corresponding to the current location and the current time; and   generating a plurality of probabilities that the first entity will be at corresponding destinations of the plurality of possible destinations at a next time subsequent to the current time.   
     
     
         11 . The system of  claim 7 , wherein the operations further include:
 predicting a second location for a second entity at the particular time; and   determining a plan to avoid the second entity using the first location and the second location.   
     
     
         12 . The system of  claim 7 , wherein the operations further including adding, using data received from a mobile communication device associated with the first entity, a new location-time pair. 
     
     
         13 . The system of  claim 7 , wherein the plurality of location-time pairs includes at least one location-time pair based on a scheduled event. 
     
     
         14 . A tangible non-transitory computer-readable medium having program instructions stored therein that, in response to execution by a computer system, causes the computer system to perform operations including:
 receiving location data for a first entity, wherein the location data includes a plurality of location-time pairs;   identifying, using a range of time values based on a particular time, a subset of the plurality of location-time pairs;   determining, using the subset of the plurality of location-time pairs, respective probabilities that the first entity will be located in the respective locations specified in the subset of the plurality of location-time pairs; and   predicting, using the respective probabilities, a first location for the first entity at the particular time.   
     
     
         15 . The tangible non-transitory computer-readable medium of  claim 14 , wherein determining the respective probabilities includes:
 determining a number of times a given location occurs within the subset of the plurality of location-time pairs; and   generating, by dividing the number of times the given location occurs within the subset of the plurality of location-time pairs by a number of location-time pairs included in the subset, a particular probability of the respective probabilities.   
     
     
         16 . The tangible non-transitory computer-readable medium of  claim 14 , wherein predicting the first location for the first entity includes determining a largest probability in the respective probabilities. 
     
     
         17 . The tangible non-transitory computer-readable medium of  claim 14 , wherein the operations further include, in response to determining a current location of the first entity is available:
 determining a second subset of the plurality of location-time pairs that correspond to a plurality of possible destinations from the current location at a current time, wherein the second subset of the plurality of location-time pairs includes a particular location-time pair corresponding to the current location and the current time; and   generating a plurality of probabilities that the first entity will be at corresponding destinations of the plurality of possible destinations at a next time subsequent to the current time.   
     
     
         18 . The tangible non-transitory computer-readable medium of  claim 14 , wherein the operations further include:
 predicting a second location for a second entity at the particular time; and   determining a plan to avoid the second entity using the first location and the second location.   
     
     
         19 . The tangible non-transitory computer-readable medium of  claim 14 , wherein the operations further include adding, using data received from a mobile communication device associated with the first entity, a new location-time pair. 
     
     
         20 . The tangible non-transitory computer-readable medium of  claim 14 , wherein the plurality of location-time pairs includes at least one location-time pair that is based on a scheduled event.